Does the Delphi process lead to increased accuracy in group-based judgmental forecasts or does it simply induce consensus amongst judgmental forecasters?

We investigate the relative impact of internal Delphi process factors - including panelists' degree of confidence, expertise, majority/minority positioning - and an external factor, richness of feedback - on opinion change and subsequent accuracy of judgmental forecasts. We found that panelists who had low confidence in their judgmental forecast and/or who were in a minority were more likely to change their opinion than those who were more confident and/or in a majority. The addition of rationales, or reasons, to the numeric feedback had little impact upon panelists' final forecasts, despite the quality of panelists' rationales being significantly positively correlated with accurate forecasts and thus of potential use to aid forecast improvement over Delphi rounds. Rather, the effect of rationales was similar to that of confidence: to pull panelists towards the majority opinion regardless of its correctness. We conclude that majority opinion is the strongest influence on panelists' opinion change in both the ‘standard’ Delphi, and Delphi-with-reasons. We make some suggestions for improved variants of the Delphi-with-reasons technique that should help reduce majority influence and thereby permit reasoned arguments to exert their proper pull on opinion change, resulting in forecast accuracy improvements over Delphi rounds.     

Technology level evaluation methodology based on the technology growth curve

Technology has been the driving force of development for knowledge-based economies. As competition in technology innovation among nations becomes more intense, there is a growing need for improved judgment, evaluation and prediction of scientific technology capacity in order to enhance national competitiveness. Until now, a country's technology level has been evaluated on a relative basis by comparing it with that of the country with the world's best technology. However, this kind of static methodology makes the interpretation of results unclear and makes time series analysis difficult. One of the most important limitations of this methodology is that it cannot be used to establish a strategy to improve the technology level. This paper examines the methodological problems of technology level evaluation and develops a dynamic methodology by applying the technology growth curve model. We also analyze the real technology level by using a new model application and review the relevance of this method. Finally, we discuss how to use the results in order to create a dynamic technology strategy.     

On the forecasting of the challenging world future scenarios

Logistic and power law methodologies for both retrospective and prospective analyses of extended time series describing evolutionary growth processes, in environments with finite resources, are confronted. While power laws may eventually apply only to the early stages of said growth process, the Allee logistic model seems applicable over the entire span of a long range process. On applying the Allee logistic model to both the world population and the world gross domestic product time series, from 1 to 2008 AD, a projection was obtained that along the next few decades the world should experience a new economic boom phase with the world GDP peaking around the year 2020 and proceeding from then on towards a saturation value of about 142 trillion international dollars, while the world population should reach 8.9 billion people by 2050. These results were then used to forecast the behavior of the supply and consumption of energy and food, two of the main commodities that drive the world system. Our findings suggest that unless the currently prevailing focus on economic growth is changed into that of sustainable prosperity, human society may run into a period of serious economical and social struggles with unpredictable political consequences.     

Patent indicators for monitoring convergence - examples from NFF and ICT

The blurring of boundaries between hitherto distinct scientific disciplines, technologies or markets is a common and powerful phenomenon. Concentrating on monitoring convergence through patent indicators, we discuss convergence with examples from the area of Nutraceuticals and Functional Foods (NFF)/Cosmeceuticals as well as information technologies, consumer electronics, and telecommunications (ICT). We analyze 7455 scientific and patent references on phytosterols with the aid of SciFinder Scholar and 3836 documents employing STN AnaVist. Furthermore, we test an IPC co-classification based approach on 859,469 ICT-related and 341,846 NFF-related patents. Our results show clear indications for convergence and a proof of principle for our monitoring concept. Academics may better evaluate environmental parameters, like cases of convergence, influencing companies' actions. Implications for practitioners are based on a more reliable assessment prior to the forming of strategic alliances or mergers and acquisitions. This could help avoid costly adventures such as the mergers and acquisitions seen in ICT.     

Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan

This work presents a novel gray-based cost efficiency (GCE) model that integrates the gray forecasting model into a two-factor cost efficiency curve model for renewable energy (RE) technologies and identifies the optimal forecasting model for power generation cost of RE technologies. The analytical framework of proposed GCE model improves short-term prediction of power generation cost, and can be applied during the early developmental stages for RE technologies. Empirical analysis is based on wind power data for Taiwan. Time lag of knowledge stock was simulated to represent the actual relationship between R&D expenditures and cost reductions in power generation by knowledge stock. Analytical results demonstrate the GCE model is a useful tool to quantify the influences of cost reductions in power generation. The implications of analytical results are that institutional policy instruments play an important role in RE technologies achieving cost reductions and market adoption. The proposed GCE model can be applied to all high-technology cases, and particularly to RE technologies. The study concludes by outlining the limitations of the proposed GCE model and directions for further research.     

基于案例推理的非常规突发事件资源需求预测

近年来非常规突发事件频繁发生,严重危害了人民的生命财产安全,而如何及时预测灾害发生后的资源需求成为一个重要课题。文章提出了一种精确的预测方法,将模糊集理论,神经网络Hebb学习规则和多元线性回归与案例推理法相结合。这种方法很好地解决了非常规突发事件资源需求预测这类信息不完备、不精确问题,能够比较准确的作出资源的需求预测。该模型对灾害资源需求预测具有一定的参考价值。     

Linking words in economic discourse: Implications for macroeconomic forecasts

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.     

Data revisions to German national accounts: Are initial releases good nowcasts?

Data revisions to national accounts pose a serious challenge to policy decision making. Well-behaved revisions should be unbiased, small, and unpredictable. This article shows that revisions to German national accounts are biased, large, and predictable. Moreover, with use of filtering techniques designed to process data subject to revisions, the real-time forecasting performance of initial releases can be increased by up to 23%. For total real GDP growth, however, the initial release is an optimal forecast. Yet, given the results for disaggregated variables, the averaging out of biases and inefficiencies at the aggregate GDP level appears to be good luck rather than good forecasting.     

基于灰色理论的中国人口空间分布模式预测

运用灰色理论对我国各个省市未来人口总量进行预测,并应用GIS的空间自相关理论和方法对未来人口空间分布模式做出预测。研究认为,从2005年到2020年中:北京、天津、江苏、上海、浙江等省市始终保持与周围省市人口密度的高高关联关系,围绕这些省市的省份区域形成了相对人口聚集区域,西部许多省份及东北几省与周围的省份人口密度是维持着低低关联模式。广东省始终保持与周围省市人口密度的高高关联关系,即本省只是单独形成了一个聚集点。2014年是我国人口空间分布格局变动较大的时期。     

A review of Delphi forecasting research in tourism

The Delphi technique is the most popular judgemental forecasting method in tourism studies, but theoretical and empirical developments in this area (especially for forecasting purposes) have been slow. This paper analyses published research on Delphi forecasting in tourism and hospitality, explores how the Delphi forecasting method has progressed over the past four decades in terms of topical areas, empirical applications, and issues of reliability and validity, and is thus expected to advance understanding of the Delphi technique, providing topical and methodological recommendations for researchers and industry practitioners for producing accurate forecasts. The literature concerning the qualitative and quantitative applications of Delphi forecasting in tourism is mainly divided into three research themes: event forecasting, forecasting tourism demand, and forecasting future trends/market conditions (the most popular application). Issues of accuracy, reliability, and validity, as well as a group of Delphi-specific characteristics, such as panel size, panel selection, consensus measures, and analysis of results, are summarized and discussed. This study also examines the accuracy of Delphi forecasts as well as exploring the role of the Delphi approach in integrating human judgement into quantitative forecasts.